Key takeaways

  • Data management is a sub-function of the data governance framework, and data management executes the rules established by the data governance framework on the tools and processes used to collect, store, cleanse, integrate, and share data per data governance policies.
  • Despite Artificial Intelligence and automated Machine language (ML) automation technology being more efficient at cleansing and processing data than humans, human involvement is necessary to program and test these data management AI tools to ensure they produce accurate and valid results.

Data management is an essential practice that is necessary when businesses want data properly handled throughout an organization. The driving influence of data management is data governance, meaning that data governance sets the policies and guidelines for proper data usage. Data management adheres to governance guidance and policies on how data is handled and utilized in an organization, making it an essential component and pillar of the data governance framework.

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What is data management?

Data management is the process of collecting, organizing, and using data securely and efficiently in cost-effective methods, all derived from adhering to data governance practices. Data management employs the technical aspects of data handling, collecting, storing, and transforming, including data access. While data management focuses on how data is handled, data governance focuses on the strategic framework that includes the standards on how data is used and managed responsibly throughout an organization.

Definition and core concepts 

Data management’s goal is to use the data governance principles by stipulating the approved methods used to process, manipulate, and share data within an organization while maintaining the four pillars. Data management is one of the four pillars of data governance. The other pillars of data governance are:

  • Data quality means the data is accurate, complete, consistent, reliable, and valid.
  • Data protection and compliance ensures data is secure and compliant with regulations and safe from data breaches and any type of malicious attacks. 
  • Data stewardship ensures the data is accessible, usable, and secure, including the lifecycle aspects of creating, preparing, using, storing, and properly disposing of obsolete data.

Importance in modern business environments

Data management is critical in many aspects of a business’s daily operations. Data management is vital because the data is used in decision-making, regulatory compliance, and identifying new trends and potential markets to exploit using innovative approaches that can improve revenue streams based on data management practices.

Types of data management

There are several types of data management, but a good data management program begins with data governance, followed by data cleansing, data architecture, data modeling, and the Extract, Transform, and Load (ETL) process, which is also covered under data governance.

Data governance

Establishes the policies and procedures for managing data quality, security, access control, and ensuring compliance with any regulations.

Data cleansing

Data cleansing is a crucial step due to identifying and correcting errors or inconsistencies with collected data, ensuring accuracy.

Read more: Data Cleaning: Definition, Techniques, & Best Practices

Data architecture

It is a visual display of how data flows within a business, including storage and access points.

Data modeling 

Visual diagrams that illustrate the different relationships between data elements and how the data moves through an organizational system.

Extract, Transform, Load (ETL)

The ETL process is an approved data governance process for extracting data from multiple data sources, transforming the data, and loading the data into a target system.

Data integration

An established technical process that combines data from multiple sources to create a consolidated view.

Data warehousing 

A centralized repository for storing large volumes of structured data used for data analytics.

Data security

Security is also an essential requirement covered by the data governance framework because it protects businesses’ proprietary data from unauthorized access, prevents data breaches, and ensures compliance with external or internal regulations.

Benefits of data management 

The data management program is a sub-program under data governance. When data management adheres to a fully functioning data governance program, one of the more tangible benefits is the competitive advantage it provides by allowing a business to make faster and more accurate decisions than its competitors. A good data management program helps improve businesses’ overall operations in several categories:

  • Improved decision-making: Allows businesses to accurately analyze data that produces data-driven decisions, which enhances a business’s efficiency.
  • Enhanced security: Data management approved processes help protect data from theft, loss, and breaches.
  • Improved operational efficiency: Data management streamlines internal operations by identifying and reducing redundancies, which eliminates wasted time with data not related to a specific goal or objective.
  • Scalability: Automated data management systems can rapidly handle an increase in a seasonal demand or any increased activity not anticipated without introducing errors in the system due to increased demand.
  • Backup and restore: Reduces any downtime by allowing an organization to recover from a data loss quickly and minimizes the risk of losing data permanently.
  • Data integration: Facilitates combining data from different sources to present a unified view of data for a general or specific audience.

Challenges of data management 

The primary challenge of data management is addressing poor data quality in a dataset or database. Poor data quality can be incorrect, inaccurate, incomplete, duplicate, or inconsistent, which are the ways data can be identified as poor. Other challenges to data management are:

Data governance

The typical data user will not understand the value of a data governance program because it’s not apparent to a typical user how data is cleansed and integrated into a dataset as value-added data to a business. Therefore, educating users on how data is handled and processed correctly is essential. Still, a broader overview of the data governance program is required to explain the roles of data management and governance. 

The data governance and data management programs are Chief Information Officer (CIO) supported programs, and there are consequences for not adhering to the authorized data management practices. A mandatory annual overview of the data governance program and the complexities associated with governance will ensure users are trained and held accountable for the proper use of data.

Data security

Data security is another topic that needs to be covered in a data governance training program that stresses the approved security protocols and access controls that focus on preventing unauthorized access and data breaches.

Regulatory compliance 

A misstep in regulatory compliance can lead to significant fines when a compliance violation is discovered. A robust governance policy must be strictly enforced to ensure compliance, particularly when data is stored in the cloud.

Data integration

Any time data is consolidated from multiple data sources, the targeted dataset or database is exposed to potential errors. Data cleansing is essential before the combined data is displayed in a unified view.

Best practices of data management

The best practices for data management begin with a data governance framework derived from an organization’s strategic objectives. The objective can be improving customer support, boosting revenue, increasing market share, or improving operational efficiency. The best practices are:

Data governance framework

A data governance framework outlines roles, responsibilities, and processes for effective data management. Establishing data policies and standards that focus on how data is collected, stored, accessed, and used in a business is vital. The standards address guidelines for naming conventions, data formats, and metadata. The policies address compliance, data quality, privacy, security, usability, integrity, retention, and authorized business tools.

Data lifecycle management

Lifecycle management addresses how long data should be archived, retained, and the proper disposal procedures. A data lifecycle management program helps organizations maintain compliance with data retention regulations, manage the risk of retaining irrelevant data, and reduce storage costs.

Master Data Management (MDM)

The MDM ensures the data is consistent and accurate across the organization, and the MDM establishes a centralized repository for master data such as product information, employee records, and other relevant data a business uses.

Data documentation and metadata management

Proper data documentation management ensures all data sources are documented and clearly defined, giving context and meaning to data that users can understand.

Data analytics and reporting

Data analytics is used to identify trends, opportunities, and anomalies typically not identified by humans. Data analytics methods, techniques, and reporting can extract actionable insights from business data used to make informed, data-driven decisions, making the data analysis process vitally important for successful businesses.

Data audits and compliance checks

Regularly scheduled audits of your data management processes and practices can help identify areas that need improvement. These checks ensure compliance regulations are upheld while preventing possible legal or compliance risk exposure.

Data security and privacy

Protecting data is paramount in preventing an organization’s data from being exposed through unauthorized access or a data breach. Ensuring businesses follow privacy regulations like the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) are mandatory. Annual security training is a must to build a cultural-conscience security organization.

Data integrations 

Data integration can be challenging when moving siloed data from multiple sources to one target system. An automated integration tool can expedite the process by enabling data to flow seamlessly into a target system, eliminating the need for manual entry or transformation. Automated data integration vastly improves data accuracy and consistency, resulting in error reduction and faster results displayed in a unified view.

Continuous data quality assurance

Data quality assurance is essential in continually identifying and correcting errors, duplications, and inconsistencies. Automated data quality assurance software like CloverDX, which is always running, can help keep your target dataset reliable and trustworthy.

The future of data management 

Large and complex datasets, known as big data, use data mining and continue to be used by businesses, making data management and governance critical processes to manage these large datasets. The volume, velocity, and variety of structured, semi-structured, and unstructured data require pre-defined data management processes that ensure data is protected and used correctly by abiding by data management practices.

Recap of key points

Data management is a program under data governance that adheres to guidance provided by approved data governance policies, processes, and roles. Data management is the operational side of data governance, which dictates how data is handled daily. Data governance and management work together to ensure data is valid, accurate, protected, and available.

Artificial Intelligence (AI) and Machine Learning (ML) tools will become everyday tools used in data management to help businesses interpret large datasets for improved decision-making. Augmented analytics uses AI and machine automation that will help transform how data is prepared, shared, and generated. Data Fabric is a data management and integration framework that allows businesses to connect and manage data across different systems and applications in real-time. 

Final thoughts on the importance of effective data management

Effective data management optimizes operations, improves decision-making, enhances customer experiences, promotes innovation, and maintains compliance by providing valid, reliable, accessible, and secure data. Data is a valuable asset, and data management is the foundational process that allows data to become valued information businesses use to make informed decisions and optimize business processes.

Frequently Asked Questions (FAQ)

Data management is the process of collecting, storing, and using data efficiently, securely, and effectively. It involves organizing data to ensure its quality, accessibility, and integrity, which enables organizations to make better-informed decisions.

Examples of data management include data storage solutions, data quality management, data governance policies, data security protocols, data integration processes, and data analysis techniques.

The four main types of data management are data governance, data quality management, data integration, and data security. Together, they ensure data accuracy, accessibility, consistency, and protection.

Data management skills encompass abilities like data analysis, database management, data governance, data security, data quality assurance, and data integration. These skills help professionals handle data accurately, securely, and efficiently for optimal organizational use.

Don Hall Avatar
Don Hall
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Don Hall is a contributing writer to Technology Advice and Enterprise Storage Forum, where he covers data storage technology, storage hardware and software, and data networking. He worked for more than two decades as an IT Supervisor for the federal government and as IT Operations Supervisor for an IT Military Command managing programmers, cybersecurity staff, and infrastructure and networking personnel. Previously he worked as an application programmer. Don earned a B.S. in Business Information Systems from San Diego State University and has certificates in Technical Communication and web development with an emphasis in Java/Open Source. He has also had an active CompTIA Security + (ce) since 2011, and a Network +(ce) since 2015.